{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据生成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = np.random.normal(10,1,(1,3))\n",
    "b = np.random.uniform(-1,1,1)\n",
    "x = np.random.normal(0,1,(1000,3))\n",
    "y = np.random.normal(w*x+b,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "都使用正态分布作为先验，其均值随着马尔可夫链的更新而更新，方差决定MH的步长。\n",
    "当接受概率低时降低步长（方差），当接受概率高时提升步长（方差）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dist_prob(w_,b_,sig):\n",
    "    log_p = stats.norm.logpdf(y,w_*x+b_,scale=sig).sum()\n",
    "    return log_p\n",
    "def replace(alpha,n_value,old_value):\n",
    "    if np.random.binomial(1,alpha)==1:\n",
    "        return n_value\n",
    "    else:\n",
    "        return old_value\n",
    "\n",
    "T = 5000\n",
    "w_b = np.ones((T,5))*0.5\n",
    "sigma = 1\n",
    "t = 0\n",
    "alpha = np.zeros((T,5))\n",
    "prob = np.zeros(T)\n",
    "for t in tqdm(range(1,5000)):\n",
    "    w_ = w_b[t-1,:].copy() # 将斜率、截距和残差放入\n",
    "    \n",
    "\n",
    "    for i in range(5):\n",
    "        if i <4:\n",
    "            w1 = stats.norm.rvs(loc=w_[i], scale=alpha[t-100:t,i].mean()*0.1 if t>100 else 0.5) \n",
    "        if i==4:\n",
    "            \n",
    "            w1 = stats.norm.rvs(loc=w_[i], scale=0.05) # 残差也用正态分布作为先验，但小于0的不计算\n",
    "            if w1 <0:\n",
    "                w1=w_[-1]\n",
    "        w_[i] = w1\n",
    "        alpha[t,i] = min(1, np.exp((dist_prob(w_[:3],w_[-2],w_[-1])) - \n",
    "                        dist_prob(w_b[t-1 ,:3],w_b[t-1,-2],w_b[t-1,-1])))\n",
    "        w_[i] = replace(alpha[t,i],w1,w_b[t-1,i])\n",
    "    w_b[t,:] = w_\n",
    "\n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果还行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,10))\n",
    "for i in range(5):\n",
    "    ax = plt.subplot(2,3,i+1)\n",
    "    ax.plot(np.arange(5000),w_b[:,i])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1p7WFqqr/AF5K8t6udA3w3AhbWm1eBLYleWv3b+wa3uRfdA/7KptavKuB64GDSZ7pap+pqodH15LWiD8H7u2ud/UT4E9G3M+qUVVPJPkG8DS9M+R+wJv8kgxehkGSGuLyjiQ1xNCXpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDfk/P4W0v6bFLLAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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q6ar6X+Bh4HdH3NMovJ5kHUC3PLVSJzL0zzNJQm9e92hVfXnU/QxLVd1eVeurapzeG3n/UlXNvOKrqn8Djif5ja50HfDiCFsalteArUne3/3Zv44G3sAe4CCws1vfCTyyUica+v+cpTldA3wSOJzk+a72uap6bHQtaUj+Enig+16qnwJ/MeJ+VlxVPZPkG8Bz9O5c+xHv8a9jSPIgcC2wJskU8HngDuBAklvo/SC8ccXO79cwSFI7nN6RpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakh/weT5sfsmI7mIgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HLVOf6RduBX4myfrudMdrgAdWOSf0nCoiyQuAn2OQe5L65H2UwTMiuvPXLwG+tqopB/p8317Q3QbwDuCOCTzD6Osg8LbuXTNXAN+qqicmHaoFSV4EfBx466ievU/FaZlaYDqDJL/R3f4XwJuB30xyCvgfYFd1Lz2vxbxV9UCSTwP3Ak8DH6iqed8mNems3aq/DPx9VX13tTMO65n394EPJfkKg1MJv9s9O1qLWV8GfDjJaQbvnrputXOekeSjDN51tjHJceD9wHnwvayfZPCOmWPAfzN4hjQxi+VN8kLgCPCDwNNJ3s3g3Uqr/suzx759H/BDwE3dMeqpWuFMkU4/IEkNmpbTMpKkJbDcJalBlrskNchyl6QGWe6S1CDLXZIaZLlLUoP+H38MCzxcfHvrAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "for i in range(5):\n",
    "    # ax = plt.subplot(2,3,i+1)\n",
    "\n",
    "    plt.hist(w_b[:,i],bins=200)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[8.93214297 9.85007825 7.80830813]] [-0.14314734] 1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 8.92011935,  9.8157372 ,  7.84517814, -0.12205731,  0.99136587])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(w,b,1)\n",
    "w_b[2000:].mean(axis=0)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "d83df975d3fd7e9cd2b9393f36d258bee244a85bb4d399c6c89ec18ff8dd5f85"
  },
  "kernelspec": {
   "display_name": "Python 3.8.3 ('data')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
